Joint channel and power optimisation for multi‐user anti‐jamming communications: A dual mode Q‐learning approach. Issue 6 (3rd March 2022) - Zhang, Xiaobo; Wang, Hai; Ruan, Lang; Xu, Yifan; Feng, Zhibin
Abstract: In view of the anti‐tracking‐jamming problem, traditional online learning methods usually cannot analyse the jamming behaviour, and find an effective way to prevent the jamming attacks. To cope with these challenges, a novel communication/deception dual mode mechanism is proposed in this paper. Deception users are selected to send high‐power signal for jamming attraction, and form collaborative relationships with communication users. The corresponding collaborative anti‐jamming model is then constructed as a Markov game to analyse the multi‐agent decision. Based on that, a joint channel and power optimisation for multi‐user anti‐jamming communications based on dual mode Q‐learning scheme is proposed. Compared with two traditional online learning algorithms, the proposed DCAJ‐QL algorithm effectively achieves 146.5% and 80.4% higher maximum communication rate under tracking jamming conditions, and achieves 40.7% and 53.6% higher maximum communication rate under fixed jamming conditions.